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Temporal characteristics of the EEG have been extensively studied for their relationship to both sleep and epilepsy. In this work, a computational algorithm that quantifies the temporal variability of the signal based on local minima and local maxima is proposed. The temporal variability of electrocorticography (ECoG) data obtained from epilepsy patients corresponding to different pathological brain...
Fractal Analysis is the well developed theory in the Non-linear Analysis of Biomedical Signals such as Electroencephalogram (EEG). EEG Biomedical signal is essentially multi scale fractal i.e., Multifractal. Therefore, quantifying the chaotic nature and complexity of the EEG Signal requires estimation of the Generalized Fractal Dimensions spectrum where the complexity means higher variability in general...
We propose a feature extraction method based on the Volterra autoregressive model's prediction power and the data's predictability for the EEG signals to automatically detect the epileptic EEG signals from the EEG recordings. The method of determining the embedding dimension based on nonlinear prediction is applied to choose the embedding dimension of the EEG data. The proposed feature extraction...
In this paper, we present a method for epileptic seizure prediction from intracranial EEG recordings. We applied correlation dimension, a nonlinear dynamics based univariate characteristic measure for extracting features from EEG segments. Finally, we designed a fuzzy rule-based system for seizure prediction. The system is primarily designed based on expert's knowledge and reasoning. A spatial-temporal...
The study was aimed at evaluating the changes in dynamical connectivity, between interictal, preictal and ictal condition, among signals derived from StereoEEG recordings in patients with Taylor's type focal cortical dysplasia (FCD type-II), by means of Partial Directed Coherence and indexes derived from graph theory. Results showed that seizures are characterized by an increased synchronization,...
A novel abstract modeling approach for brain dynamics exhibiting epileptiform activity is proposed, and a seizure prediction algorithm based on this approach is presented. The model consists of several polytopes of parameters, each of which corresponds to a particular brain dynamics, and a seizure onset is predicted when real time-identified model parameters make a transition from an interictal polytope...
Abstract-This paper presents a new approach to recognize and predict succedent epileptic seizures by using single-channel electroencephalogram (EEG) analysis. Eight channels of EEG from each patient of the seven consenting patients with generalized epilepsy were collected in Epilepsy Center of Xijing Hospital. The raw EEGs were decomposed by the algorithm of empirical mode decomposition (EMD), the...
In this paper we assess a dependency measure for multivariate time series termed extrinsic-to-intrinsic-power-ratio (EIPR) using two different signal models. In a comparison with partial directed coherence (PDC) we show that both measures correctly identify imposed couplings, but that limitations of the PDC do not affect EIPR. Moreover, EIPR is successfully used for the localization of the seizure...
Implicit Wiener series are a powerful tool to build Volterra representations of time series with any degree of non-linearity. A natural question is then whether higher order representations yield more useful models. In this work we shall study this question for ECoG data channel relationships in epileptic seizure recordings, considering whether quadratic representations yield more accurate classifiers...
In this contribution a new algorithm based on the spatio-temporal dynamics of reaction-diffusion cellular nonlinear networks (RD-CNN) for analyzing brain electrical activity in epilepsy is proposed. RD-CNN are determined in an identification process and then analyzed by means of Chuas Local Activity theory. Clinical manifestations of epileptic seizures are phenomena of abnormal, excessive, or synchronous...
The use of both linear autoregressive model coefficients and nonlinear measures for classification of EEG signals recorded from healthy subjects and epilepsy patients is investigated. A total of seven nonlinear measures namely the approximate entropy, largest lyapunov exponent, correlation dimension, nonlinear prediction error, hurst exponent, third order autocovariance, asymmetry due to time reversal,...
In this paper, we attempt to analyze the effectiveness of the Empirical Mode Decomposition (EMD) for discriminating epilepticl periods from the interictal periods. The Empirical Mode Decomposition (EMD) is a general signal processing method for analyzing nonlinear and nonstationary time series. The main idea of EMD is to decompose a time series into a finite and often small number of intrinsic mode...
The fractal dimension (FD) is a natural measure of the irregularity of a curve. In this study the performances of two FD-based methodologies are compared in terms of their ability to detect the onset of epileptic seizures in scalp EEG. The FD algorithms used is Katzpsilas, which has been broadly utilized in the EEG analysis literature, and the k-nearest neighbor (k-NN), which is applied in this study...
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